Annotation-Efficient Segmentation: Developing an encouraging performance segmentation model always requires many high-quality annotations, but labeling the abdominal multi-organ is very expensive and time-consuming, each volume around takes 1.2-2.6 hours. To reduce the label costs, annotation-efficient learning has attracted many researchers' attention, such as semi-supervised learning [1]}, [2]}, [3]} and weakly supervised learning [4]}. In this work, we propose to learn from the scribble annotation by minimizing the entropy minimization and intra-class intensity variance minimization. Although our proposed method improves the baseline by a large margin, there is also a huge performance gap compared with dense annotations. In this work, we want to do some attempts to inspire more annotation-efficient researches in the future.
